AI Scientist - Machine Learning; US​/KR

We are seeking a highly motivated AI Scientist specializing in Machine Learning to join our growing AI R&D team. In this role, you will be at the forefront of developing and deploying cutting‑edge deep learning models to solve real‑world temporal modeling challenges in manufacturing. We’re looking for a candidate with strong practical R&D experience, grounded in solid theoretical fundamentals, and deep expertise in AI disciplines.

The ideal candidate will have a deep understanding of state‑of‑the‑art machine learning algorithms and techniques, a track record of impactful publications in top‑tier conferences such as NeurIPS, ICML, ICLR, KDD, CVPR, or ICCV, and a solid background in computer science and engineering. Experience collaborating with software engineering teams to scale and productize ML solutions is a strong plus. This is a high‑impact role that combines foundational research, system‑level design, and hands‑on implementation.

You’ll work closely with cross‑functional teams to develop innovative solutions that guide strategic decisions and deliver tangible business value.

• Design and implement Transformer‑based architectures for time‑series prediction and sequence modeling, across both univariate and multivariate data.
• Drive the full machine learning lifecycle—from exploratory data analysis to model deployment, monitoring, and continuous improvement.
• Conduct rigorous benchmarking, ablation studies, and performance optimization to ensure robustness and efficiency.
• Collaborate closely with data scientists, engineers, and product managers to translate complex business requirements into scalable technical solutions.
• Partner with software engineers to scale and productize ML algorithms within manufacturing AI software products.
• Contribute to Gauss Labs’ intellectual property portfolio through patents and high‑impact technical publications.
• Mentor junior team members and play an active role in shaping the team’s AI roadmap and long‑term strategy.

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
• 3+ years of hands‑on experience in deep learning, with a strong focus on sequence modeling and time‑series forecasting.
• In‑depth expertise in Transformer architectures and their applications beyond natural language processing.
• Proficiency in Python and deep learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Solid mathematical foundation in statistics, optimization, and signal processing.
• Familiarity with hybrid modeling approaches that combine deep learning and traditional statistical methods.
• Experience working with noisy, sparse, or irregularly sampled time‑series data.
• Strong publication track record in top‑tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR).
• Practical experience deploying ML models in production environments, with knowledge of MLOps best practices.